Build-to-Order Supply Chain Management 9781846636950, 9781846636943

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31/10/2007

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ISSN 0144-3577

Volume 27 Number 11 2007

International Journal of

Operations & Production Management Build-to-order supply chain management Guest Editor: Angappa Gunasekaran

The official journal of the European Operations Management Association

www.emeraldinsight.com

International Journal of

ISSN 0144-3577

Operations & Production Management

Volume 27 Number 11 2007

Build-to-order supply chain management Guest Editor Angappa Gunasekaran

Access this journal online _________________________ 1139 Editorial board ___________________________________ 1140 Guest editorial ___________________________________________ 1141 Creating the customer-responsive supply chain: a reconciliation of concepts Andreas Reichhart and Matthias Holweg ___________________________ 1144

Mix flexibility and volume flexibility in a build-to-order environment: synergies and trade-offs Fabrizio Salvador, Manus Rungtusanatham, Cipriano Forza and Alessio Trentin _______________________________________________ 1173

Modularization and the impact on supply relationships Mickey Howard and Brian Squire ________________________________ 1192

A strategic model for agile virtual enterprise partner selection Joseph Sarkis, Srinivas Talluri and A. Gunasekaran _________________ 1213

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CONTENTS

CONTENTS

Information technology and performance management for build-to-order supply chains

continued

Amir M. Sharif, Zahir Irani and Don Lloyd ________________________ 1235

Bi-negotiation integrated AHP in suppliers selection Yee Ming Chen and Pei-Ni Huang ________________________________ 1254

Call for papers ___________________________________ 1275

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International Journal of Operations & Production Management Vol. 27 No. 11, 2007 p. 1140 # Emerald Group Publishing Limited 0144-3577

EDITORIAL BOARD Professor David Bennett Aston Business School, Birmingham, UK

Professor Bart MacCarthy Nottingham University Business School, UK

Professor Will Bertrand Eindhoven University of Technology, The Netherlands Dr Kate Blackmon Said Business School, University of Oxford, UK

Professor Douglas Macbeth University of Southampton School of Management, UK Professor Jose´ Machuca University of Seville, Seville, Spain

Dr Ruth Boaden Manchester Business School, UK

Professor Jack Meredith Wake Forest University, USA

Professor Harry Boer University of Aalborg, Aalborg, Denmark

Professor Andy Neely Cranfield School of Management, UK

Professor Ken Boyer Michigan State University, USA

Professor Christopher C. New Vivenda Constancia, Algarve, Portugal

Professor Richard B. Chase Marshall School of Business, USA

Dr Carol Prahinski Michigan State University, USA

Professor T.C. Edwin Cheng Hong Kong Polytechnic University, Hong Kong

Professor Jaume Ribera IESE Business School, Spain

Professor Henrique Luiz Correa Crummer Graduate School of Business, Rollins College, Florida, USA

Dr Nick Rich Lean Enterprise Research Centre, Cardiff University, UK

Professor Paul Cousins Manchester Business School, UK

Professor Giovani da Silveira University of Calgary, Canada

Dr Simon Croom University of San Diego, USA

Professor Nigel Slack Warwick Business School, Coventry, UK

Associate Professor Joy M. Field Boston College, USA Professor Robert Handfield College of Management, North Carolina State University, USA

Professor Amrik S. Sohal Department of Business Management, Monash University, Australia

Professor Christer Karlsson Copenhagen Business School, Denmark

Professor Andrea Vinelli University of Pa´dova, Vicenza, Italy

Professor Mike Lewis School of Management, University of Bath, UK

Professor Chris Voss London Business School, UK

Assistant Professor Rui Sousa Universidade Cato´lica Portuguesa, Porto, Portugal

Guest editorial Build-to-order supply chain management The build-to-order (BTO) supply chain model has been actively pursued in several different industries. One of the first successful BTO companies was Dell Computers, which gained market share by building customized computers using the internet as an order fulfillment vehicle. The BTO supply chain can be defined as the configuration of firms and capabilities of the supply chain that support a higher level of flexibility and responsiveness to changing customer requirements in a cost effective manner. The aim of this feature issue is to help researchers and practitioners to understand the implications, development and management of BTO supply chains. With the help of articles appearing in this feature issue, one should be able to better understand the design and implementation of the BTO supply chain. This special issue on BTO supply chains deals with the following: (1) product design decisions in the BTO supply chain; (2) supplier or partnership selection; (3) information technology in the BTO supply chain; and (4) performance management in the BTO supply chain. The six articles appearing in this issue present researchers and practitioners with analytical and simulation models, and conceptual and empirical frameworks for developing and managing the BTO supply chain. They deal with a range of problems from product design to customer services in BTO supply chains. An overview of the papers is provided below to highlight the key contributions of each of them to the theory and applications of this topic. The concept of supply chain responsiveness (SCR) has received considerable attention in the operations management literature, mostly under the auspices of concepts such as build-to-order, mass customization, lean and agility. However, there is a lack of a suitable framework that comprehensively portrays the cause-and-effect relationships involved in SCR. In the paper, “Creating the customer-responsive supply chain: a reconciliation of concepts” Reichhart and Holweg aim to address such a gap. The paper synthesizes the existing contributions to manufacturing and supply chain flexibility and responsiveness, and draws upon various related bodies of literature that affect a supply chain’s responsiveness such as product architecture and modularization. They have identified four types of responsiveness: product, volume, mix and delivery, all of which can relate to different time horizons, and can be present as either potential or demonstrated responsiveness. Furthermore, they propose a holistic framework distinguishing between requiring and enabling factors for responsiveness, identifying the key relationships within and between these two categories. The framework developed in this paper is suited for both qualitative and holistic quantitative studies. The second paper, “Mix flexibility and volume flexibility in a build-to-order environment: synergies and trade-offs” by Salvador, Rungtusanatham, Forza and Trentin investigates the factors enabling or hindering the simultaneous pursuit of

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volume flexibility and mix flexibility within a supply chain through the lens of a manufacturing plant seeking to implement a BTO strategy. To accomplish this empirical investigation, they designed an in-depth case study involving a manufacturing plant and its supply chain. Their empirical study suggests that, to some extent, volume flexibility and mix flexibility may be achieved synergistically, as initiatives such as component standardization or component-process interface standardization would improve both volume flexibility and mix flexibility. Howard and Squire in their paper, “Modularization and the impact on supply relationships” examine the role of product architecture in supply chain design. Specifically, they seek to resolve confusion over the impact of modularization on supplier relationship management. The introduction of modularization suggests that buyer and supplier firms should move towards greater collaboration in order to co-develop products and reduce interface constraints. The paper supports the argument that modularized components require collaborative sourcing practices; this suggests that outsourcing requires a high level of integration, creating dependencies between firms, and representing considerable investment in equipment and sharing through proprietary information systems. As interest in build-to-order supply chains and flexible product architecture grows, this emphasizes the importance of specifying the exact nature of relationship processes without satisfying product innovation. The paper, “A strategic model for agile virtual enterprise partner selection” by Sarkis, Talluri and Gunasekaran provides a multi-attribute model to help in the evaluation and selection of partners within virtual enterprise environments. After development of the many factors necessary for forming an agile virtual enterprise, the analytical network process (ANP) is used as the multi-attribute model to link these factors. The results show that the normative ANP model is an effective method to help in the integration of the complex set of organizational and intra-organizational factors for selecting partners for an agile virtual enterprise. This paper seeks to integrate tangible and intangible factors and multi-organizational selection for AVE formation. The penultimate paper, “Information technology and performance management for build-to-order supply chains” by Sharif, Irani and Lloyd addresses the growth and importance of BTO supply chains, which allow consumers and supply chain participants to select, configure, purchase and view order delivery status. This paper uses an interpretivist case study research strategy that exploits multiple research methods. Also, it focuses on a case study in which an aerospace components company was attempting to become a BTO enterprise. Finally, the paper, “Bi-negotiation integrated AHP in suppliers selection” by Chen and Huang proposes a new approach for tackling the uncertainty and imprecision of identifying suitable supplier offers, evaluating these offers and choosing the best alternatives in bi-negotiation. In a BTO supply chain, the handling of uncertainties is addressed by a real time information sharing system and appropriate supplier selection. A methodology integrated analytic hierarchy process (AHP) with bi-negotiation agents based on the multi-criteria decision-making approach and software agent technique is then developed to take into account both qualitative and quantitative factors in supplier selection. During the decision-making between buyer and suppliers, the AHP process matches product characteristics with supplier characteristics. Next, agents assist the user in the debate to negotiate a joint representation of the chosen supplier and automatically justify proposals with this

joint representation. This study focused on a multi-attribute negotiation mechanism including qualitative conditions, which enables automated negotiation on multiple attributes. Finally, a fuzzy membership function represented the joint representation’s cognition for each condition such as quantity, price, quality, and delivery for the outsourced component. A case study in a high-end computer manufacturing company is given to demonstrate the potential of the methodology. In summary, this special issue of IJOPM on the BTO supply chain offers great scope both in terms of theory and applications. Considering the growing importance of outsourcing and information technology, the BTO supply chain is poised to become the twenty-first century operations paradigm incorporating the perspectives of virtual enterprise and internet-enabled supply chain management. I hope the readers will find this special issue interesting and useful not only from a research point of view, but also from applications in practice. Finally, I would like to thank all the authors who have submitted papers for the special issue of the BTO supply chain and the reviewers who have reviewed manuscripts in a timely manner to realize the special issue on time. I am most grateful to the Editors of IJOPM for their constant support right from the beginning until the editorial had been approved. Without the help of the Editors, a quality special issue of this nature on the BTO supply chain can hardly be realized. Angappa Gunasekaran Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts-Dartmouth, North Dartmouth, Massachusetts, USA E-mail: [email protected]

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The current issue and full text archive of this journal is available at www.emeraldinsight.com/0144-3577.htm

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Creating the customer-responsive supply chain: a reconciliation of concepts

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Andreas Reichhart and Matthias Holweg Judge Business School, University of Cambridge, Cambridge, UK Abstract Purpose – While the concept of supply chain responsiveness (SCR) has received considerable attention in the operations management literature, mostly under the auspices of concepts such as build-to-order, mass customisation, lean and agility, so far there is a lack of comprehensive definition of SCR, as well as a defined relationship between “responsiveness” and “flexibility”. Also, the frameworks at hand tend to consider only a subset of factors previously identified in the literature, and thus do not comprehensively portray the cause-and-effect relationships involved. This paper aims to address these gaps. Design/methodology/approach – The paper synthesises the existing contributions to manufacturing and supply chain flexibility and responsiveness, and draws on various related bodies of literature that affect a supply chain’s responsiveness such as the discussion of product architecture and modularisation. Findings – Four types of responsiveness are identified: product, volume, mix, and delivery, all of which can relate to different time horizons, and can be present as either potential or demonstrated responsiveness. It is argued that a supply chain can feature different levels of responsiveness at different tiers, depending on the configuration of the individual nodes, as well as the integration thereof. Furthermore, a holistic framework is proposed, distinguishing between requiring and enabling factors for responsiveness, identifying the key relationships within and between these two categories. Research limitations/implications – The definition and framework proposed provide novel insights into the concept of SCR as well as a clear terminology that will inform future research. The framework developed in this paper is suitable for both qualitative and holistic quantitative studies. Originality/value – In addition to a detailed review of the factors associated with SCR, a generic definition of responsiveness is developed. The paper proposes a definition of four types of responsiveness which will support further empirical studies into the concept and its application. Furthermore, a holistic framework is developed that allows for cause-and-effect relationships to be investigated and dependencies to be identified. Keywords Supply chain management, Response flexibility, Bespoke production Paper type General review

International Journal of Operations & Production Management Vol. 27 No. 11, 2007 pp. 1144-1172 q Emerald Group Publishing Limited 0144-3577 DOI 10.1108/01443570710830575

1. Introduction In market conditions of increasing levels of product variety and customisation, the ability to respond to customer orders in a timely fashion can provide a critical competitive advantage. Across industry sectors, such as fashion (Christopher, 2000; Storey et al., 2005), personal computers (Kapuscinski et al., 2004), consumer electronics (Catalan and Kotzab, 2003), construction (Arbulu et al., 2003), and automobiles (Holweg and Pil, 2004), companies are contemplating strategies to increase their responsiveness to customer needs by offering high product variety with short lead-times. More recently, the discussion of mass customised products (Lampel and Mintzberg, 1996; Gilmore and Pine, 1997) has shifted the discussion beyond the simple provision of

product variety towards individually customised products. While these customer-driven or build-to-order (BTO) strategies have been implemented in the personal computer sector, with Dell being the most prominent example (Kapuscinski et al., 2004), complex manufacturing operations, such as automotive, have been slower in adopting these strategies (Hertz et al., 2001; Holweg and Pil, 2004). The increasing importance of BTO supply chains results from two developments: first, the number of product variants has been increasing across most industries, such as consumer electronics (Catalan and Kotzab, 2003), fashion and sportswear (Fisher et al., 1994), and automobiles (Holweg and Pil, 2004). Second, time has become a factor in competitiveness as customers are increasingly reluctant to accept long lead-times for products and services (Bower and Hout, 1988; Stalk, 1988). The former development creates severe operational problems for traditional make-to-forecast or “push” strategies, as firms require large amounts of finished goods inventories to ensure customers find the specifications they are looking for. In the case of the Mercedes E-Class, which is available in more than three septillion (3 £ 1024) variations (Pil and Holweg, 2004) for example, a make-to-forecast strategy becomes virtually impossible. While BTO strategies can help overcome the first hurdle, existing auto supply chains are not sufficiently responsive to deal with the second development; impatient customers, who would like their vehicles delivered within 2-3 weeks, rather than the current six weeks (Holweg et al., 2005a). Across industry sectors, the concept of responsiveness has been receiving increasing attention in the operations management literature, and has been advanced as one of the key themes in recent supply chain research. However, we regard supply chain responsiveness (SCR) not as an operations paradigm in its own right, but rather as a concept that can implicitly rest at the core of various operations strategies, such as lean thinking (Womack and Jones, 1996, as described by Hines et al., 2004), agility (Goldman and Nagel, 1993) and more recently, BTO supply chain management (Gunasekaran and Ngai, 2005). Common to all of these is the importance of customer-oriented pull systems, as opposed to traditional forecast-based mass production systems (Skinner, 1969; Hill and Chambers, 1991; Fisher, 1997). Responsiveness thus is a crucial aspect of BTO supply chains, yet not one that is confined to them, as the fashion industry with some highly responsive non BTO supply chains demonstrates (Christopher, 2000). We argue that the importance of responsiveness in today’s industry settings, in conjunction with a wealth of contributions that have blurred the boundaries to related concepts, such as flexibility (Slack, 1987; Upton, 1994), agility (Goldman and Nagel, 1993; van Hoek et al., 2001; Yusuf et al., 2004), and lean thinking (Womack and Jones, 1996; Hines et al., 2004), justifies a review of this body of literature, and the subsequent attempt to propose a set of clear definitions. In particular, a key shortcoming of the existing definitions of responsiveness is their unclear separation from the definitions of flexibility. Furthermore, SCR is a goal that can be achieved through multiple means, the appropriateness of which largely depends on various product and market related characteristics. A framework that provides a holistic approach to responsiveness, explaining the interdependencies within and between its external requirements and internal determinants, is still missing. Thus, the main objectives of this paper are: . to propose a clear definition of SCR and its relationship to flexibility; and . to develop a holistic framework, capturing the individual factors that require and enable the responsiveness of a supply chain system.

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The first section of the paper will define responsiveness and its relation to the concept of flexibility drawing mainly on existing definitions of flexibility and responsiveness, both in manufacturing systems and supply chains, and supported by systems terminology (Ackoff, 1971). The second section will review the literature on the requirements and determinants of responsiveness towards the proposition and explanation of a conceptual framework of SCR. Finally, conclusions, managerial implications and areas for future research will be presented. 2. Towards a generic definition of responsiveness There is considerable ambiguity in the existing literature with regards to the differences between responsiveness and flexibility. Furthermore, both terms are often used to describe features of manufacturing systems (Slack, 1987; Gindy et al., 1999) and entire supply chains (Handfield and Bechtel, 2002; Lummus et al., 2003), and are at times used interchangeably, yet do not capture exactly the same concept. In addition, within the last decade two new terms have been introduced, “agility” and “leagility” (Goldman and Nagel, 1993; Naylor et al., 1999), which postulate flexibility and responsiveness in manufacturing operations, organisations and supply chains as a key tenet of a firm’s competitiveness. Historically, the first contributions on flexibility focused entirely on manufacturing systems, and were only later extended to entire supply chains. Responsiveness on the other hand is a term that has only more recently been introduced as a distinct, independent concept in the operations literature. Table I lists the key contributors to the flexibility and responsiveness streams and their respective scope (manufacturing system vs supply chain), which will be discussed in detail below.

Manufacturing system level

Flexibility

Responsiveness

Zelenovic´ (1982)

Gindy et al. (1999)

Lim (1987)

Supply chain level

Slack (1987) Gerwin (1993) Gupta (1993) Upton (1994) Lau (1999) Vokurka and O’Leary-Kelly (2000) Vickery et al. (1999) Garavelli (2003) Lummus et al. (2003) Wadhwa and Rao (2004)

Table I. Flexibility – responsiveness – agility: contributors

Agility

Goldman and Nagel (1993) Matson and MacFarlane Burgess (1994) (1999) Mileham et al. (1999) Holweg (2005a) Holweg (2005b)

Lau and Lee (2000) Handfield and Bechtel (2002) Catalan and Kotzab (2003) Harrison and Godsell (2003) Randall et al. (2003)

Naylor et al. (1999) Christopher (2000) Mason-Jones et al. (2000) Herer et al. (2002) Towill and Christopher (2002) Yusuf et al. (2004)

In this section, we aim to consolidate these concepts into a generic definition of responsiveness, and to define its relationship to flexibility. The literature on the definition of flexibility and its classification is reviewed, initially focussing on manufacturing systems and then extending it to include entire supply chains. Subsequently, the concept of responsiveness in manufacturing systems and supply chains is discussed, reviewing existing definitions. Finally, a generic definition including the relationship between flexibility and responsiveness is proposed and its implications and assumptions are discussed. 2.1 Flexibility in manufacturing and supply chain systems The contributions on flexibility in manufacturing systems are numerous (Gerwin, 1993; Upton, 1994; Lau, 1999) and difficult to summarise (de Toni and Tonchia, 1998). In their literature review of manufacturing flexibility, de Toni and Tonchia (1998) propose a scheme for analysing this vast body of knowledge according to six aspects, only two of which are relevant for this part of the paper: (1) the definition of flexibility; and (2) the classification of flexibility. The latter is similar to what Garavelli (2003) calls the “object of change” which according to him, is the most interesting aspect of the flexibility discussion from an operational perspective. It is often conceptualised based on the seminal contributions by Slack (1987) and Upton (1994), who found several types of flexibility in manufacturing systems along two and three dimensions, respectively[1]. Slack identifies four types of flexibility: product, mix, volume and delivery. He also identifies two dimensions for each type of flexibility: range and response. Range refers to the maximum number of different outcomes a resource with the respective flexibility type can achieve, such as the total number of different products a given machine can produce. “Response” refers to the time and cost with which different values within a range can be achieved (e.g. setup time and cost for switching between two products). Slack also concludes that three types of manufacturing resources can be utilised to achieve flexibility, namely flexible technology, labour and infrastructure. Upton (1994) is less specific with regards to the types of flexibility that exist. He identifies up to 15 different types, out of which he only exemplifies four in the case studies discussed: product, volume, business area and size. These 15 types are also used by Vokurka and O’Leary-Kelly (2000), while other authors report varying types and numbers (see de Toni and Tonchia, 1998 for a comprehensive review). Expanding on Slack’s (1987) framework, Upton identifies a third dimension of flexibility: uniformity, which refers to the ability of a resource to provide consistent performance throughout its entire range. In addition, Upton draws attention to a time aspect in the flexibility discussion by saying that firms can be flexible at the operational, tactical and strategic level, each one focusing on different time horizons, thereby confirming previous arguments made by Carlsson (1989) and Zelenovic´ (1982). Upton further concludes that there are two different aspects of flexibility which he refers to as “internal” vs “external” flexibility. External flexibility can be linked to achieving a competitive advantage, such as speed of delivery (what the customer sees). Internal flexibility on the other hand is the internal means by which external flexibility can be achieved (what can we do).

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Drawing on Ackoff (1971) and previous definitions of flexibility (Zelenovic´ 1982), flexibility can thus be defined as the ability of any system to adapt to internal or external influences, thereby acting or responding to achieve a desired outcome. Following Slack (1987) and Upton (1994), a system’s flexibility is based on internal resources that can be used to achieve different types of internal flexibility, which in turn can support the system’s ability to demonstrate external flexibility to its environment. External flexibility can focus on short-, medium- or long-term goals and will hence require operational, tactical or strategic internal flexibility to achieve them. The above definition based on flexibility in manufacturing systems can also be extended to entire supply chains, in the same way that traditional production and inventory control research has been extended from single to multi-tier manufacturing systems. Vickery et al. (1999) and Lummus et al. (2003) provide the two most prominent examples of this extension. Vickery et al. (1999) identify five distinct flexibilities for a supply chain which include product, volume, new product, distribution and responsiveness flexibility. As described by Lummus et al. (2003, p. 3), the extension from manufacturing to supply chain flexibility follows the logical extension of a manufacturing system to a complete supply chain: (. . .) this extension involves looking at those components that make an organisation flexible and extends them beyond the organization’s boundaries to other nodes in the supply chain. Hence, the definition of flexibility does not change per se, only its internal types may be modified to include inter-company considerations, now that an entire supply chain is the unit of analysis, as opposed to a single node (manufacturing system) within a supply chain. 2.2 Responsiveness in manufacturing and supply chain systems Matson and MacFarlane (1999, p. 765) define production responsiveness as: (. . .) the ability of a production system to achieve its operational goals in the presence of supplier, internal and customer disturbances. Those disturbances clearly relate to the three types of uncertainty explained by Davis (1993), namely supply, process and demand uncertainty. McCutcheon et al. (1994) regard responsiveness as equal to the delivery lead-time for a certain product. Therefore, a manufacturing system would be regarded as more responsive, if it could deliver the same product with a shorter lead-time. Holweg (2005a, p. 6) uses a more general, yet very similar definition and argues that responsiveness is: (. . .) the ability of the manufacturing system or organisation to adapt to changes and requests in the marketplace. A similar definition is also used by Hines (1998), who refers to the responsiveness of the supply chains studied using the supply-chain response matrix (Hines and Rich, 1997). This approach considers the total inventory held at all stages in a supply chain in combination with the throughput time at each stage. Hines (1998) thus suggests that those supply chains are more responsive that can switch to a new product within a shorter time period, because they can: . push new products faster through the entire supply chain; and . keep less stock of the “old” product that needs to be used first. Catalan and Kotzab (2003, p. 677) define responsiveness of a supply chain in a comparable way, namely: (. . .) as the ability to respond and adapt time-effectively based on the ability to “read” and understand actual market signals.

The above definitions, despite their differences, show considerable similarities. First, the majority of them link responsiveness to changes or outcomes required by the system’s external environment. Second, they usually include some time or effort dimension, i.e. those systems that are regarded as more responsive can adapt to these changes faster than others, a concept that matches the “response” dimension of flexibility discussed above. However, Matson and MacFarlane’s (1999) definition is not fully consistent with the other definitions in as far as they link responsiveness to internal as well as external requirements (disturbances). Thus, the question remains whether a system that can react quickly to internal disturbances, such as machine break downs (for manufacturing systems) or supplier problems (for supply chains), should be considered responsive or not? The majority of authors seem to link responsiveness exclusively to external events (e.g. changes in customer demand), which is supported by Ackoff (1971, p. 664), who defines the response of a system as: (. . .) a system event for which another event that occurs to the same system or to its environment is necessary but not sufficient; that is, a system event produced by another system or environmental event (the stimulus).

A system can therefore only respond to external stimuli but not to internal events alone[2]. Responsiveness should thus be considered as a concept that is solely customer focused, and its measurability depends on where the system boundaries are drawn and thereby on the definition of the system’s customers. As discussed above, there is no general agreement on the number of internal flexibility types in manufacturing systems, let alone in supply chains. Hence, it is proposed that a system’s external flexibility, i.e. the flexibility that a customer might be “interested in” consists of the four types identified by Slack (1987): product, mix, volume and delivery. Product flexibility describes the ability to introduce new products or changes to existing products. Mix flexibility is the ability to alter the product mix (within the existing product range) that the system delivers. Volume flexibility refers to the ability to change the system’s aggregated output and delivery flexibility is the ability to alter agreed delivery agreements (e.g. shortening lead-times or even changing the products’ destination). If there are in-sequence delivery arrangements, such as in the automotive component industry, delivery flexibility also includes the ability to make changes to the agreed delivery sequence. Figure 1 shows the relationship between flexibility and responsiveness based on the flexibility dimensions identified by Slack (1987) and extended by Upton (1994), and incorporating the four external and various internal flexibility types discussed above. Responsiveness is therefore constituted by the system’s response dimension of its external flexibility types. For the purpose of this and further studies, the following definition will be adopted: The responsiveness of a manufacturing or supply chain system is defined by the speed with which the system can adjust its output within the available range of the four external flexibility types: product, mix, volume and delivery, in response to an external stimulus, e.g. a customer order.

The types of internal flexibility required on the other hand will be contingent upon the types of responsiveness demanded as well as upon the specific operational setting. Most importantly it needs to be acknowledged that such relationships exist, i.e. mix responsiveness might, for example require flexibility in machinery. A customer would

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Types of Flexibility Dimension

Internal

Product

Mix

Volume

Delivery

Machinery

Operations

Routing

Figure 1. Relationship between flexibility and responsiveness

Expansion

Response

Others

Uniformity

Program

1150

Material handling

Range

External

Responsiveness

however not be interested in how his supplier is able to meet changes in product mix demands, as long as the supplier can adjust the product mix to suit the customers’ needs without negatively affecting other requirements, such as product quality. Similarly, a customer would not be interested in whether his supplier is sufficiently responsive to meet a large unforeseen order because the supplier itself is responsive (e.g. through buffer stocks of purchased components) or because the supplier’s suppliers can deliver the required components quickly. From a customer’s point of view his supplier’s manufacturing system’s responsiveness is thus the same as the supply chain’s responsiveness. Hence, a shift in the focus from manufacturing systems to supply chains is driven by other factors. Traditionally, supplier flexibility was seen as an operational factor impacting upon a manufacturing system’s performance (Vokurka and O’Leary-Kelly, 2000), yet with the increasing complexity in today’s supply chains it is argued that the flexibility of manufacturing systems in a supply chain should be regarded as a factor contributing to a supply chain’s responsiveness and not vice versa, at least for firms, which want to compete on the basis of BTO supply chains. It is also important to note that responsiveness will differ at different nodes in the system. The raw material supply node in some industries, for example is known for its unresponsiveness (in terms of large batch sizes and long lead-times), whereas tier-one suppliers are often required to be highly responsive (Holweg, 2005a). Thus, a supply chain’s responsiveness can increase or decrease as one moves downstream in the supply network, similar to the way that quality levels can increase or decrease within supply chains (Hines, 1998). SCR can thus be both an objective of a node in the supply chain, and a requirement for its downstream nodes. At every node in a supply chain system, the supply chain also has a potential (what could it do) and a demonstrated responsiveness (“what does it do” Upton, 1994). Only when these are aligned, can a responsive supply chain be created that is also cost-efficient. Looking at the time classification of flexibility, responsiveness can be split into at least short- and medium-term responsiveness, too. A supply chain’s short-term or operational responsiveness is its ability to adjust its output to short-term demand changes.

These changes can be due to changes in the product mix (mix responsiveness), the volumes required (volume responsiveness), or the delivery sequence or timing (delivery responsiveness). New products are rarely introduced at short notice, and product responsiveness probably only exists in the medium- and long-term horizon. What exactly short-term means however depends on the industry and supply chain node under consideration; in fast clockspeed settings such as electronics (where products can have a life cycle of as low as two months) it will be very different from those with slow clockspeed settings, like automotive, where products stay in production for an average of six years (Holweg and Pil, 2004). 3. Towards a framework for responsiveness 3.1 Approach to literature review In the following, a framework for the study of SCR will be developed based on a review of a broad range of relevant literature, mainly from the operations management stream. The framework serves multiple objectives: first and most importantly, the aim is to consolidate the contributions of previous frameworks (Table II) and individual factors into a single comprehensive framework. Second, the framework should be generic in nature in order to serve as a basis for further holistic studies for researchers from all perspectives of the epistemological spectrum. This in turn requires two-sided measurements (dependent vs independent variables), as opposed to a mere index creation, used for example by van Hoek et al. (2001). Last, the interdependencies within and between the external requirements and internal determinants of SCR need to be clearly stated, as these cause-and-effect relationships are central for building responsive supply chains. This section will start by summarising the key contributions available on SCR and flexibility, categorised by their main characteristics and the respective factors considered (Table II). Subsequently, the individual factors related to SCR will be discussed. Last, these factors will be consolidated into a holistic framework of SCR. At prima facie, there is a considerable overlap in the frameworks summarised in Table II, yet there is neither agreement on which individual factors and concepts to include nor on how to group them. A major shortcoming of the majority of the existing frameworks is the lack of distinction between factors that require supply chains to be responsive and factors that enable them to be responsive (Kritchanchai and MacCarthy, 1999). The subsequent literature review and framework development will apply this distinction, and cover “external requirements”, i.e. factors that require responsiveness, followed by “internal determinants”, i.e. factors that enable responsiveness. This grouping facilitates a structured analysis of the concept of SCR in at least two ways: first, it illustrates the underlying cause-and-effect relationships that exist in supply chains. For example, it is not the supply chain’s responsiveness that changes by removing external requirements, such as external demand variability, but rather the need for the supply chain to be responsive, a fact that has in the past not always been considered (Harrison, 1996). Second, it can guide either customers of a supply chain or firms therein to identify ways to influence the supply chain’s suitability for its environment. This will further facilitate matching different supply chains to their environment as suggested by Fisher (1997). The literature review will be structured according to the individual factors associated with requiring or enabling responsive supply chains. Such a structure

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Author, year

Characteristics of framework

Factors considered

Kritchanchai and MacCarthy (1999)

Framework for comparing cross-industry responsiveness of the order fulfilment process

van Hoek et al. (2001)

Framework measuring a supply chain’s “agile capabilities” based on five dimensions of agility

Catalan and Kotzab (2003)

Responsiveness index (rating) based on four components grouped into two broad categories: time effective flow of goods and information and demand transparency Analysis split into components and outcomes of supply chain flexibility Five components and two outcomes

Nature of product Demand Major impact stimuli Awareness Capabilities Goals Customer sensitivity Virtual integration Process integration Network integration Measurement Lead time (production and distribution lead-time) Postponement strategies Bullwhip effect Information exchange Components Operations systems Logistics processes Supply network Organisational design Information systems Outcomes Customer satisfaction (including service and responsiveness) Improved supply chain asset utilisation Customer lead-times Volume stability Demand specifications (Pareto) Product variety (external, internal) Point of customisation Product life cycle Total order-to-delivery (OTD) time Distribution lead-time Supply chain response lead-time Decoupling points

1152

Lummus et al. (2003)

Holweg (2005b)

Table II. Frameworks for analysing SCR/flexibility

Three dimensions of responsiveness (volume, product, process)

appears appropriate, since the majority of non-conceptual contributions investigate individual factors as opposed to the holistic concept of SCR. The factors discussed below mainly originate from the frameworks presented in Table II, yet the individual sections also draw extensively upon the large body of literature that focuses on those factors individually. 3.2 External requirements The need for supply chains to become responsive can be derived from the contributions listed in Table II, even though they are rarely included in the frameworks. The four main areas are:

(1) (2) (3) (4)

Customerresponsive supply chain

demand uncertainty; demand variability; product variety; and lead-time compression.

Table III gives an overview over these factors, which also provide the structure for the following discussion of the external requirements. 3.2.1 Demand uncertainty and variability. A range of previous studies identify uncertainty as the main reason for being responsive (Davis, 1993; Fisher et al., 1994; Randall et al., 2003). Under conditions of reliable information about demand conditions, there would hardly be a need to be responsive. The need arises mainly from the uncertainty that stems from volume and or product mix changes in the customer demand signal. Many studies have investigated the factors that make responsiveness or flexibility a necessary feature of either individual manufacturing systems (Azzone and Masella, 1991; Matson and MacFarlane, 1999) or entire supply chains (Fisher et al., 1994; Christopher, 2000) and therein uncertainty is always mentioned as the root cause for becoming flexible or responsive, either directly or indirectly. Uncertainty itself can emanate from three different sources, namely supply uncertainty, process uncertainty and demand uncertainty (Davis, 1993). Of these, demand uncertainty is regarded as the most severe type (Davis, 1993; McCutcheon et al., 1994).

Factor Demand uncertainty

Main links to SCR

Main requirement for being responsive, i.e. 100 per cent reliable demand would considerably reduce need for responsiveness Important sub-category is schedule instability, particularly important for industries operating under rolling schedules Demand Often closely linked to demand uncertainty, variability yet conceptually different Even if demand was 100 per cent reliable, large swings (even if known) in demand could still require responsiveness Product Product variety further increases demand variety uncertainty Product variety can directly increase the need for mix responsiveness High product variety increases the cost of using finished good inventories to fill orders Lead-time Directly increases need for responsiveness, as less compression time is available to respond to customer orders Indirectly increases need for responsiveness through increased demand uncertainty (changes in P:D ratio)

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Key contributionsa Davis (1993), Fisher et al. (1994), Harrison (1996), Griffiths and Margetts (2000) and Krajewski et al. (2005) Harrison (1996) and Inman and Gonzalves (1997)

McCutcheon et al. (1994), MacDuffie et al. (1996), Berry and Cooper (1999) and Randall and Ulrich (2001) Bower and Hout (1988), Mather (1988), Stalk (1988) and McCutcheon et al. (1994)

Note: aThe contributions listed specifically link the respective factor to SCR

Table III. External requirements of SCR

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An interesting sub-category of demand uncertainty is demand uncertainty under rolling-horizon planning/scheduling (Simpson, 1999; Dellaert and Jeunet, 2003; Krajewski et al., 2005), which is the prevailing method in repetitive manufacturing environments. Short-term schedule changes or even daily sequence changes for in-sequence suppliers operating under rolling-horizon schedules can occur when the updated schedule does not match the previous one. The impact of schedule instability on supply chains is very significant and it has been discussed by numerous authors (Inman and Gonzalves, 1997; Griffiths and Margetts, 2000; Krajewski et al., 2005), who consistently regard it as a primary cost driver in the supply chain. Oliver et al. (1994) even conclude from their investigations into the differences between “world-class” and “non world-class” automotive component plants that demand stability ranked amongst the most important factors for overall plant performance. In addition to schedule instability (or demand uncertainty in general), schedule variability (or demand variability) is mentioned in relation to responsiveness (Harrison, 1996). Here, a clarification needs to be made between two schedule attributes: schedules can be stable by not deviating from the previous schedule or forecast, and can be level (i.e. not variable), whereby the day-to-day changes are kept small within predefined boundaries (a concept also referred to as “heijunka” which literally translates into “smooth wave”). Inman and Gonzalves (1997) and Harrison (1996) in reference to Bhattacharya et al. (1995) argue for a difference between stable schedules and level schedules with regard to their impact on supply chains, yet the existing literature on schedule variations (both instability and variability) has often neglected to clearly differentiate between these two concepts (Liker and Wu, 2000). Both demand uncertainty and variability are inherent to most operations and can require different types of responsiveness depending on their nature (i.e. product mix vs volume vs delivery changes). Thus, depending on the type of demand uncertainty or variability, different internal capabilities might be required, at least on a manufacturing system level (Suarez et al., 1995). The impact of uncertainty can be observed in many supply chains. Hewlett-Packard for example, relied on high inventory levels to buffer against demand uncertainty in their supply chain until they realised that some product configuration decisions could be postponed by using a more modular product architecture (Davis, 1993; Feitzinger and Lee, 1997). While they could not reduce the demand uncertainty originating from end customers, they moved to a less costly strategy to deal with the problem. In a true BTO supply chain however, such as in the case of the Volvo Car Group (Hertz et al., 2001), investments in flexible manufacturing (e.g. through mutable support structures) are generally more suitable than late configuration or postponement (Pil and Holweg, 2004). 3.2.2 Product variety. One of the prevailing streams in the literature on the management of product variety is on its implications for the firm’s wider performance (Lancaster, 1990; Da Silveira, 1998; Ramdas, 2003; Pil and Holweg, 2004). The need for managing product variety results both from the competitive importance of product variety in today’s markets (McCutcheon et al., 1994; Lampel and Mintzberg, 1996; Gilmore and Pine, 1997), and its potential financial impact for product development activities and manufacturing operations. As Fisher et al. (1994) and Randall and Ulrich (2001) explain, demand uncertainty is amplified by product variety, as the same aggregated demand is split over more stock keeping units (SKUs), leading to an increase in the aggregated errors associated with each forecast. In addition, product

variety increases the need for mix responsiveness as the range of the external mix flexibility increases (Berry and Cooper, 1999), and customers are not willing to accept longer lead-times. Those problems have also led firms to rethink the level of product variety that is really demanded by their customers (Fisher et al., 1994; MacDuffie et al., 1996). Holweg and Pil (2004) differentiate between three dimensions of product variety. First, external variety (also referred to as product proliferation by some authors: see Gupta and Srinivasan, 1998) refers to the number of SKUs (including their variations) available to a firm’s customers at any point in time. Second, internal variety refers to the complexity within a firm’s manufacturing processes and can be approximated by the number and variety of components required for manufacturing a given product. Clearly, a high level of modularisation (Starr, 1965) helps limit the internal variety under a given external variety. Internal variety or rather product architecture should however be seen as an internal determinant of responsiveness and will be discussed later. Last, dynamic variety mainly refers to shortened product life cycles, i.e. it refers to the speed with which consumers will be given access to new products. Several authors (Davis, 1993; Fisher et al., 1994) have argued that the demand for a product will always be harder to predict at the beginning of its life cycle, as no past demand pattern for this product is available, and because customers may react unexpectedly to the new product. Higher dynamic variety thus further increases demand uncertainty. Despite being primarily an external requirement, product variety can also directly inhibit supply chains from being responsive since it makes the use of finished goods buffer stocks more costly (Holweg and Pil, 2004). In practice, the product variety that a company in a given industry wants to offer to its customers should determine the supply chain strategy (Fisher, 1997), but it is at the same time also restricted by its supply chain capabilities. Dell for example, a company known for its responsive assemble-to-order supply chain, is able to offer virtually any possible variation of a PC or laptop computer to its customers, because the computer is only assembled once the exact specifications are known (Holweg and Pil, 2004). Most of Dell’s competitors however, who sell made-to-forecast computers through retail outlets, are forced to limit product variety to a few variations. This means that a customer who would like to have a faster CPU might also have to take (and ultimately pay for) a larger hard drive, even though it might not be required. 3.2.3 Lead-time compression. Time-based competition (Bower and Hout, 1988; Stalk, 1988; McCutcheon et al., 1994) by definition increases the need to be responsive, because the firm or supply chain is given less time to respond to new orders or changes in existing ones. Mather (1988) provides another explanation for why lead-time compression requires additional responsiveness. Using the P:D ratio, a concept dating back to work by Shingo (1989), he explains how the forecasting horizon becomes longer if the customer lead-time “D” decreases in relation to the production lead-time “P”. The longer the time horizon that needs to be forecasted however, the less reliable the forecast becomes (Mather, 1988; Randall and Ulrich, 2001), which in turn increases demand uncertainty. 3.3 Internal determinants The internal determinants identified during the literature search can be grouped into two broad categories. The first are factors that focus mainly on individual nodes within

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the supply chain (i.e. manufacturing systems). Second are factors that deal with the integration of supply chain partners. The former will be referred to as “operational factors” whereas the latter are commonly called the dimensions or factors of “supply chain integration” (Morash and Clinton, 1998; Lee, 2000). The existing literature on SCR (Table II) tends to focus on supply chain integration, even though it is often acknowledged that single node factors, such as manufacturing responsiveness, will contribute to the responsiveness of the overall supply chain, and that the benefits of single node factors can be increased by supply chain integration (Lummus et al., 2003). The identification and discussion of the operational factors thus mainly relies on a broad review of the operations management literature. While the discussion of supply chain integration also draws upon such a broad review, the concept itself has been discussed by various authors already (Morash and Clinton, 1998; Lee, 2000; Bask and Juga, 2001; van Hoek et al., 2001), yet rarely are the same terminology or grouping of sub-factors used. van Hoek et al. (2001) for example, use the terms “virtual integration” to describe information sharing and the integration of information systems, “process integration” to describe the alignment of processes to manage change and overcome internal uncertainties, and “network integration” to describe the focus on common goals as opposed to single firm competition. Lummus et al. (2003) on the other hand use the categories “information systems” “logistics processes” and “supply networks” to cover broadly the same content. For the following discussion, the grouping will be based on Lee (2000) and Bagchi and Skjoett-Larsen (2002). Thus, supply chain integration will be split into “information integration” “coordination and resource sharing” “organisational integration” and a fourth integration factor that emerged during the literature review: “spatial integration and logistics”. All factors identified are summarised in Table IV. 3.3.1 Demand anticipation. One of the most obvious enablers for a supply chain to be responsive is for its members to be able to anticipate their actual demanded output. Fisher et al. (1994) suggest “accurate response” systems to counter demand uncertainty by differentiating which of the products offered can be forecasted more accurately than others. For easy-to-forecast products, less demanding supply chain strategies can be used, saving flexible resources for hard-to-forecast products (Fisher, 1997). de Treville et al. (2004) investigate different demand anticipation and information exchange strategies and develop what they refer to as the three levels of relative supply lead-time, a concept based on Mather’s P:D ratio (Mather, 1988). They conclude that improving demand anticipation is only part of the story and that the overall P:D ratio must be improved to become more responsive. This they link to required improvements in combined production lead-times. 3.3.2 Manufacturing flexibility. SCR by definition requires the individual manufacturing systems to be responsive (i.e. flexible on their external response dimension). In our literature review, however, we did not find any previous study which differentiated the factors of manufacturing flexibility by their impact on any specific dimension. Nevertheless, a reduction in the system’s throughput time (the “P” in Mather’s P:D ratio) is certainly a determinant of responsiveness as it allows for products to be delivered to customers without the need for market anticipation or costly inventories of finished goods (Hines, 1998). A decrease in the throughput time of the overall system can be achieved by various measures, such as a reduction in logistics lead-times (Davis, 1993), faster information processing (de Treville et al., 2004)

Spatial integration and logistics

Organisational integration

Coordination and resource sharing

Information integration

Note: aThe contributions listed specifically link the respective factor to SCR

Supply chain integration

Product architecture/postponement

Inventory

Manufacturing flexibility

The accurate anticipation of demand can help supply chains to respond to customer requirements faster Traditional focus of the flexibility and responsiveness discussion Can directly reduce the production lead-times and change-over times for products in supply chains Can both increase and decrease the responsiveness of supply chains Often used as buffer against uncertainty Closely linked to the decoupling point, which is a common criterion for classifying supply chain strategies, such as build-to-order supply chains Determines to a large extent where the decoupling point can be placed and thus how responsiveness can be achieved Determines manufacturability and internal product variety/complexity Can help reduce internal demand amplification and eliminate delays due to slow information flows Eliminating unnecessary demand uncertainty and variability facilitates a better focus on end customer demand Removes delays and unnecessary activities in supply chains and leverages synergies Reduces demand variability and uncertainty Can increase the responsiveness and general performance of supply chains in various ways Particularly important impact on trust, which is required for a variety of interactions between supply chain members A reduction in transport lead-times directly increases the responsiveness of supply chains Can further strengthen process coordination and organisational integration

Operational factors

Demand anticipation

Main links to SCR

Factor

Collins et al. (1997), Frigant and Lung (2002), Larsson (2002) and Reichhart and Holweg (2005)

Burbidge (1961), Lee (2000) and Holweg et al. (2005b) Sako and Helper (1998), Bagchi and Skjoett-Larsen (2002), Droge et al. (2004) and Perona and Saccani (2004)

Fisher et al. (1994) and de Treville et al. (2004) Slack (1987), Mather (1988), Upton (1994) and Blumenfeld et al. (1999) Shingo (1989), Hoekstra and Romme (1992), Davis (1993), Turnbull et al. (1993), Womack and Jones (1996) and Holweg and Pil (2004) Starr (1965), Ulrich (1995), Feitzinger and Lee (1997), Lee and Tang (1997) and Pagh and Cooper (1998) Forrester (1958), Lee et al. (1997), Christopher (2000) and Gunasekaran and Ngai (2004)

Key contributionsa

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Table IV. Internal determinants of SCR

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and factors directly linked to manufacturing flexibility like shortened machine changeover times (Shingo, 1989; Mileham et al., 1999). A large body of knowledge on manufacturing flexibility and its positive effects on supply chains and lead-time reductions is available (Lim, 1987; Slack, 1987; Gerwin, 1993; Upton, 1994; Blumenfeld et al., 1999; Gindy et al., 1999), and was reviewed inter alia, by de Toni and Tonchia (1998) and Beach et al. (2000). Manufacturing flexibility should however not be seen as the only solution for achieving system flexibility as pointed out by Lau (1999) who further explains that managers should also include other functions in their company as well as their suppliers, an argument that has formed the basis for supply chain management since its inception (Houlihan, 1985). 3.3.3 Inventory. Despite a tendency across industries to become “lean” and operate with less stock (Womack et al., 1990; Womack and Jones, 1996), inventory buffers still exist in many places in supply chains (Davis, 1993; Hines, 1998; Holweg and Pil, 2004). Turnbull et al. (1993) reported that when required to deliver just in time to their customers, smaller suppliers were still relying on inventories instead of synchronised production, an approach that Womack and Jones (1996, p. 88) were also observing: “Most of the applications of JIT, even in Japan, have involved Just-in-Time supply, not Just-in-Time production.” As Davis (1993) further explains, more than half of the total stock in Hewlett Packard’s supply chain existed as a buffer against demand uncertainty. Buffer stocks can clearly increase a supply chain’s volume, mix or delivery responsiveness, even if they might reduce a supply chain’s product responsiveness (Hines, 1998), yet Griffiths and Margetts (2000) conclude that such buffer stocks can also easily offer a false sense of security, while hiding problems in the underlying processes – a message strongly supported by related research from the system dynamics field. Shingo (1989) further differentiates between two types of stock: “naturally” occurring and “necessary” stock. The latter becomes necessary due to process inefficiencies, such as stock produced to offset weakness in the production process in terms of quality, or stock produced due to a P:D ratio of greater than one. However, many recent contributions argue that in the case where the P:D ratio cannot be reduced to below one, decoupling points (Hoekstra and Romme, 1992; Olhager, 2003) and thus inventory are required in supply chains (Naylor et al., 1999; Christopher, 2000). The decoupling point in a supply chain is the inventory point at which end customer demand meets forecast driven production. As explained by Olhager (2003), market-, production- and product-related factors have to be considered in the positioning of the decoupling point, while Bozarth and Chapman (1996) primarily argue for a contingency on product architecture. On the one hand, a decoupling point that is close to the end-customer will lead to shortened lead-times and thus partially to increased responsiveness. On the other hand, increased inventory holding costs will be incurred due to an increase in value added together with higher product variety in later stages of the supply chain (Holweg and Pil, 2004). 3.3.4 Product architecture. Product architecture and the related concepts of modularisation (Starr, 1965; Ulrich, 1995; Baldwin and Clark, 1997), postponement and late configuration (Feitzinger and Lee, 1997; Lee and Tang, 1997; Pagh and Cooper, 1998; Pil and Holweg, 2004), are often linked to the mass customisation debate (Lampel and Mintzberg, 1996; Gilmore and Pine, 1997). Adjusting the product architecture is seen as a way to employ decoupling points to offer a wide variety of products to end

customers while reducing inventory holding costs for products with a P:D ratio of greater than one. While the personal computer industry has implemented BTO strategies through an assemble-to-order approach, this is not feasible in sectors that lack standardised component interfaces, such as in automotive (Holweg and Pil, 2004). For the latter, either a purchase and BTO strategy (Hoekstra and Romme, 1992) or late configuration/postponement is used. Postponement refers to the postponement of specification decisions in the production process to reduce initial product variety and for the main variant explosion to occur once the demand for the exact specifications is known, e.g. through a customer order (Pagh and Cooper, 1998), which can in some cases happen after the final assembly and is then usually referred to as late configuration (Holweg and Pil, 2004). Daugherty and Pittman (1995) for example, report the increasing use of late configuration in distribution centres which they thus refer to as “accessorization centers”. While late configuration can increase a supply chain’s responsiveness, decision makers need to be aware that a scenario in which the decoupling point is positioned after the final assembly operation, is no longer considered a BTO supply chain and hence does not yield the associated benefits. Furthermore, the product architecture is linked to both manufacturability and internal product variety, which can inhibit the responsiveness of manufacturing operations and therefore the responsiveness of the supply chains they are part of (Fisher et al., 1994; Christopher, 2000; Holweg and Pil, 2004). As a result, supply chain partners increasingly make product development decisions jointly (Petersen et al., 2005) and ideally align them with the respective operations strategy and supply chain structure (Fixson, 2005). 3.3.5 Information integration. The detrimental effect of a lack of demand visibility on the performance of the supply chain was pointed out by Forrester (1958) almost half a century ago. Building upon Forrester’s bullwhip effect, Towill (1997) explains how demand uncertainty (especially with regards to aggregated volumes per period) further upstream in supply chains is created to a great extent by downstream supply chain members, referring to Burbidge’s (1961) work on the effects of re-order levels on supply chains. Subsequent studies were conducted by Lee et al. (1997) and Sterman (1989) that confirmed these findings. Inman and Gonzalves (1997) however report that schedule fluctuations can also be improved by individual supply chain partners, similar to a phenomenon that Hines (1998) refers to as “quality filters” with regards to quality improvements as products flow downstream. Suggestions for improvements have been made by a variety of authors (Bagchi and Skjoett-Larsen, 2002; Gunasekaran and Ngai, 2004), which are commonly referred to as “supply chain collaboration” (Holweg et al., 2005b) or “virtual supply chain” (Christopher, 2000), and are targeted at creating transparency or visibility of both demand and capacity information in the supply chain without any time delays. Information sharing is usually achieved through the increased use of information technology or a closer integration between supply chain partners (Bagchi and Skjoett-Larsen, 2002) in order to facilitate two-way communication. Various authors also take a more critical view of the extent to which information systems can solve supply chain problems and increase their responsiveness, pointing out that other inter-organisational aspects, such as trust (McIvor et al., 2003; Akkermans et al., 2004) and further process coordination and organisational integration (Burbidge, 1961; Lee, 2000) should accompany a mere information exchange.

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3.3.6 Coordination and resource sharing. Coordination and resource sharing refers to how processes, value-adding steps and related decisions are coordinated and potentially rearranged across firm boundaries and how internal or external resources are shared to add value to products at interfaces in supply chains. Few studies have looked at the extent to which supply chain partners coordinate their processes, yet some examples can be found in related publications. Burbidge (1961) for example describes how misaligned re-order levels can create demand variability and uncertainty in supply chains. Li and Liu (2006) further demonstrate that supply chain partners can benefit from a co-ordination of quantity discount policies. One additional recent development is vendor managed inventory, an arrangement under which suppliers take responsibility for maintaining stock levels at their customers’ sites, thereby relieving their customers of re-ordering decisions (Holweg et al., 2005b). The realignment of value-adding tasks in some supply chains is described by Lee (2000) using the computer industry as an example. Collins et al. (1997) describe how similar shifts in the automotive value chain can contribute to increased SCR and general performance by leveraging core competencies and realigning complexity. The coordination of processes and achievement of synergies can be further supported by outsourcing processes at the interfaces to third parties, such as logistics service providers (Spencer et al., 1994). 3.3.7 Organisational integration. The supply chain literature features many studies of the effects of organisational integration on supply chain performance and its contributing factors (Suarez et al., 1995; Lamming, 2000; Droge et al., 2004), and the ways in which organisational integration can be achieved (Rich and Hines, 1997; Bagchi and Skjoett-Larsen, 2002; Perona and Saccani, 2004). Hill and Chambers (1991), Suarez et al. (1995), Liker and Wu (2000) and Droge et al. (2004) all agree that close organisational integration positively influences manufacturing and supply chain performance and responsiveness. The activities that constitute organisational integration can range from extensive communication to close cross-supplier integration in the case of Japanese supplier associations. This is seen as one of the competitive advantages of Japanese vehicle manufacturers (Rich and Hines, 1997). Suppliers can also employ engineers at customer sites to facilitate better communication and faster problem solving (Ikeda, 2000). Bagchi and Skjoett-Larsen (2002) provide a concise overview of the most common characteristics of organisational integration which include joint design teams, process and quality teams, joint performance measurement and problem solving, amongst others. As Wagner (2003) points out though, one needs to differentiate between supplier integration during product development and operational execution. Sako and Helper (1998) further conclude that some forms of integration can facilitate inter-firm trust, a concept further discussed below. 3.3.8 Spatial integration and logistics. Spatial integration can take many forms, from loose co-location to formalised supplier parks (Larsson, 2002). Many factors have been discussed in relation to spatial integration, such as trust and commitment (Collins et al., 1997), problem solving capabilities (Salerno and Dias, 2002), labour relations (Sako, 2003), and synergies (Larsson, 2002). The most benefit however seems to come from logistical proximity which reduces transportation lead-times and costs to a minimum, often supported by dedicated infrastructure, such as conveyor belts. The latter are of increasing importance due to just-in-time or even in-sequence deliveries (Frigant and Lung, 2002; Larsson, 2002). In addition, spatial proximity can facilitate cross-firm cost

sharing of logistics costs, for example through “milk-run” deliveries (Holweg and Pil, 2004), without negatively affecting the supply chain’s responsiveness. Rather, a reduction in transport times is likely to occur which will increase responsiveness, yet such an effect is contingent upon the form and functionality of such spatial configurations within the wider supply chain, also related to the positioning of the decoupling point, especially in supply chains that source components from overseas (Reichhart and Holweg, 2005). Spatial integration in the form of local logistics centres or formalised supplier parks are particularly common at decoupling points in BTO or assemble-to-order supply chains, like those at Dell (Kapuscinski et al., 2004) and Volvo Cars (Larsson, 2002).

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3.4 Framework consolidation Based on the discussion of the external requirements and internal determinants of SCR, the framework in Figure 2 is suggested as a conceptualisation of SCR. In addition to the factors discussed above, it also features some “relational factors”. Relational factors govern or simply influence the relationship between the supply chain and its customers. Depending on the particular supply chain and the characteristics of the transactions carried out between the partners, those factors will be more or less formalised (Williamson, 1979). Relational factors are neither external requirements nor

Internal Determinants External Requirements

Demand anticipation

Demand uncertainty

Manufacturing flexibility Operational factors Inventory

Demand variability Potential

Supply chain responsiveness (SCR)

Information integration Coordination and resource sharing Organisational integration

Demonstrated

Product architecture / postponement

External product variety

Lead-time Supply chain integration

Control factor: Ability to meet requirements (delivery reliability & quality)

Spatial integration and logistics Relational Factors Agreements / contracts Trust / commitment

Figure 2. SCR: conceptual framework

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pure internal determinants. They can be influenced by both parties and impact upon the need, as well as the ability, to be responsive. Relational factors often mitigate certain uncertainties inherent in markets and trading relationships in order to benefit from associated savings. Under rolling schedules for example, there are two important tools for limiting demand uncertainty contractually. First, so-called “frozen horizons” (usually 4-6 days for automotive manufacturers), in which the production schedule does not change, can substantially reduce the need for very short-term responsiveness (Karlsson and Norr, 1994; Harrison, 1996; Inman and Gonzalves, 1997; Holweg and Pil, 2004). Furthermore, in order to control the period prior to the frozen horizon, a so-called “quantity-flexibility” contract can be used to set upper limits for possible changes, thereby allowing the supply chain to focus its resources efficiently on those changes that are allowed (Tsay and Lovejoy, 1999; Sethi et al., 2004; Krajewski et al., 2005). For other scenarios time-based pricing has been suggested as a tool for reducing demand uncertainty and improving capacity utilisation, thereby allowing a supply chain to be more responsive to last minute urgent orders and to recover the costs thereof (Holweg and Pil, 2004; Jiang and Geunes, 2006). Time-based pricing however is a tool that has remained largely unused in most traditional industries despite its advantages for BTO supply chains and its success in many service industries, such as air travel (Holweg and Pil, 2004). Trust and mutual commitment have been partially addressed already as these are closely linked to many of the factors discussed above. However, they also constitute a relational factor in their own right. Handfield and Bechtel (2002) provide the most direct study of the relationship between trust and responsiveness and show a positive correlation between the two. They also confirm previous arguments by Dyer (1994) of links between trust and dedicated assets, which can further contribute to a supply chain’s responsiveness. Sako (1998) has shown that inter-firm trust has a positive relationship on the business performance of automotive suppliers, and in particular on responsive just-in-time delivery arrangements. Finally, a control factor is of particular importance for quantitative studies, as two different supply chains with the same external requirements do not demonstrate the same level of responsiveness if one of the supply chains can deliver products more reliably and with better quality. Therefore, delivery reliability (commonly measured as percent of on-time and in-full shipments) and product quality (commonly measured as defects per million units) were added to the framework. Depending on the particular industry setting, these specific measures can be replaced as long as some control factor measures the supply chain’s ability to meet the requirements placed upon it. Given the assumption that responsive supply chains incur higher costs than less responsive supply chains (Fisher, 1997), the two-sided approach presented in the above framework can facilitate the design of more cost-efficient supply chains. It is important to understand the framework as a tool designed to be applied to every important interface in a supply chain, starting with the end customer and going upstream. At every interface the affected supply chain partners, usually under the lead of the OEM, can evaluate how much responsiveness is really required by adjusting the external requirements and thereby the supply chain’s demonstrated responsiveness, and how this responsiveness can be best achieved through an appropriate combination of the internal determinants available. The latter translates into a supply chain’s potential responsiveness.

The first step largely reduces costs in the supply chain by reducing the need to be responsive in the first place, while the second step adjusts and reconfigures the supply chain to deliver the required level of responsiveness cost-efficiently. The “relational factors” can support these efforts either way.

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3.5 Framework application and recommendations for future research The primary purpose of the above SCR framework is to support future academic studies but it can equally be applied to managerial problems. In our view, key questions for future research include the study of responsiveness in different industry settings, and in particular responsiveness profiles of whole supply chains (i.e. the responsiveness levels at different points in a given supply chain). The framework provides a clear set of definitions and the foundation for such research without limiting its application to a particular stance within the epistemological spectrum. By differentiating between potential and demonstrated responsiveness and the respective grouping of the identified factors into internal determinants and external requirements, we have provided the basis for a two-sided measurement of this complex concept. The framework is not limited to quantitative approaches, as it does not prescribe any quantitative measure to be used for the individual factors. As such, future researchers are free to either use the framework for qualitative investigations, or if desired, refer to the quantitative studies referenced for existing measures. A second main benefit is that the framework is specific enough to focus the researcher’s attention on the key areas that enable and require responsiveness while not limiting investigations to individual nodes in the supply chain. Owing to this it can be used to measure responsiveness at single nodes as well as to create responsiveness profiles of entire supply chains. The main areas where this framework can assist future research are: . The trade-offs between responsiveness and costs. The framework allows for a comparison of internal determinants (and associated costs) against actually demonstrated responsiveness levels. A quantitative study capable of confirming the impact of individual factors on a supply chain’s responsiveness is still amiss, and this kind of study would extend the research into trade-offs in operations management (Skinner, 1969; Boyer and Lewis, 2002) from factory to supply chain level. While there seems to be wide-spread agreement that there are trade-offs between responsiveness (or more generally, flexibility) and cost-efficiency in supply chains (Fisher, 1997; Randall et al., 2003), we are not aware of any empirical study that examines to what extent entire supply chains can push their performance frontiers (Schmenner and Swink, 1998) to become more competitive. . Improved understanding of inventory in the supply chain. A well-defined responsiveness concept offers new possibilities for modelling supply chains. Instead of modelling production processes within individual echelons in detail, one could conceptualise each node in terms of its responsiveness. This can facilitate more quantitative research into explaining the operational need for inventory through the responsiveness at individual inventory locations in supply chains. . Process coordination. Another area for future investigations is how decisions and process changes at individual echelons impact on responsiveness-related costs in

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the upstream supply chain, an area where the debate has so far remained rather conceptual, anecdotal or narrative in nature. While process coordination amongst supply chain members has been identified as an important aspect of supply chain integration (see section on “external requirements”), little empirical evidence exists that illustrates and more specifically quantifies the savings that can be achieved through process coordination. Unless means are developed for this purpose, individual firms will remain reluctant to bear the initial costs of process changes in the light of unknown cost savings in other supply chain partners’ operations. For practitioners, the framework can serve two functions. First, it facilitates an understanding of how individual nodes in supply chains interact in determining the overall system’s ability to respond to customer demand, showing which factors, and thus decisions, can increase or decrease this responsiveness of the overall supply chain. This includes an understanding of which actions can be taken to reduce the need for responsiveness at every node (for example through the use of “frozen horizons” or level scheduling). Second, it can serve to benchmark the responsiveness of supply chains within an industry, bearing in mind that cross-industry benchmarks would have to further consider differences in end product architecture and complexity (Kritchanchai and MacCarthy, 1999). Practitioners can also use the framework to evaluate how they can eliminate unnecessary requirements from their suppliers by removing deficiencies from their own processes and by making more favourable product architecture decisions for the entire supply chain. 4. Conclusions In this paper, the literature on operational responsiveness and flexibility was reviewed and consolidated, taking a particular focus on concepts that relate to creating a customer-responsive supply chain system. While seminal contributions have been made (Slack, 1987; Upton, 1994) which have developed our understanding of the underlying theoretical concepts, the definitions used are at times contradictory and generally do not provide the crucial distinction between the “responsiveness” and “flexibility” concepts. Furthermore, while the individual cause-and-effect relationships provide some insights into selected linkages between the key variables, they do not provide the holistic view required to describe this complex concept. Hence, based on our review, we propose that: . There are different types of responsiveness, both in terms of the unit of change (product, volume, mix and delivery responsiveness) and in terms of the time horizon affected (short, medium or even long-term responsiveness). . A supply chain can (and is even likely to) exhibit different levels of responsiveness, depending on where in the supply chain its responsiveness is measured. . One needs to distinguish between factors that require a supply chain to be responsive and those that enable it to be responsive. This leads to the conclusion that at any point in a supply chain, the supply chain will have a potential and demonstrated responsiveness.

Moreover, we argue that, based on the framework developed, individual supply chain partners and their integration with each other can either increase or decrease an overall supply chain’s responsiveness. This proposition leads to the conclusion that there are different ways to increase the responsiveness of supply chains, some of which will be more costly than others, yet the real costs and benefits of increasing a supply chain’s responsiveness cannot be assessed by considering individual echelons only. As a consequence, a supply chain’s responsiveness can only be understood by investigating the responsiveness of its individual members and their interactions, yet such investigations should be specific about the type of responsiveness studied. In particular, we would like to argue for a two-part approach to building responsive and cost-efficient supply chains, a combination crucial to the expansion of BTO supply chains: first, a reduction of requirements at each echelon, and second, an adjustment of the internal determinants of responsiveness in the upstream supply chains. Such an approach will also ensure that – at every echelon – the supply chain’s potential and demonstrated responsiveness are aligned, avoiding unnecessary costs. We hope that the definition and framework will inform the further study of SCR by providing both a clarification of the concepts involved, and a guideline to conduct more quantitative, yet holistic investigations in this area. Notes 1. It should be noted that Upton and Slack did not use the same terminology: Upton refers to Slack’s “types of flexibility” as “dimensions” whereas he refers to Slack’s “dimensions” as “elements of flexibility”. In addition, Upton refers to Slack’s dimension of “response” as “mobility”. 2. The triggering of a system event without any external stimuli is referred to as an “act” not a “response” (Ackoff, 1971). References Ackoff, R.L. (1971), “Towards a system of systems concepts”, Management Science, Vol. 17 No. 11, pp. 661-71. Akkermans, H., Bogerd, P. and van Doremalen, J. (2004), “Travail, transparency and trust: a case study of computer-supported collaborative supply chain planning in high-tech electronics”, European Journal of Operational Research, Vol. 153 No. 2, pp. 445-56. Arbulu, R.J., Tommelein, I.D., Walsh, K.D. and Hershauer, J.C. (2003), “Value stream analysis of a re-engineered construction supply chain”, Building Research & Information, Vol. 31 No. 2, pp. 161-71. Azzone, G. and Masella, C. (1991), “Design of performance measures for time-based companies”, International Journal of Operations & Production Management, Vol. 11 No. 3, pp. 77-85. Bagchi, P.K. and Skjoett-Larsen, T. (2002), “Integration of information technology and organizations in a supply chain”, The International Journal of Logistics Management, Vol. 14 No. 1, pp. 89-108. Baldwin, C.Y. and Clark, K.B. (1997), “Managing in the age of modularity”, Harvard Business Review, Vol. 75 No. 5, pp. 84-93. Bask, A.H. and Juga, J. (2001), “Semi-integrated supply chains: towards the new era of supply chain management”, International Journal of Logistics: Research and Applications, Vol. 4 No. 2, pp. 137-52.

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Mix flexibility and volume flexibility in a build-to-order environment Synergies and trade-offs

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Fabrizio Salvador Department of Operations and Technology Management, Instituto de Empresa Business School, Madrid, Spain

Manus Rungtusanatham Department of Supply Chain Management, W.P. Carey School of Business, Arizona State University, Tempe, Arizona, USA, and

Cipriano Forza and Alessio Trentin Dipartimento di Tecnica e Gestione dei Sistemi Industriali, Universita` degli Studi di Padova, Vicenza, Italy Abstract Purpose – This paper aims to investigate the factors enabling or hindering the simultaneous pursuit of volume flexibility and mix flexibility within a supply chain through the lens of a manufacturing plant seeking to implement a build-to-order (BTO) strategy. Design/methodology/approach – To accomplish this empirical investigation, an in-depth case study involving a manufacturing plant and its supply chain was designed. Prior to primary and secondary data collection, this research setting had already decided to implement a BTO strategy and had, moreover, carefully assessed several practices for BTO strategy implementation, as well as their interactions. Findings – The studied case suggests that a number of approaches typically used to increase volume flexibility, actually negatively affect mix flexibility and vice versa. The existence of such trade-offs may ultimately inhibit the implementation of a BTO strategy and this was the case in the studied company. Nevertheless, empirical evidence also suggests that, to some extent, volume flexibility and mix flexibility may be achieved synergistically, as initiatives such as component standardization or component-process interface standardization would improve both volume flexibility and mix flexibility. Research limitations/implications – The pursuit of volume flexibility and mix flexibility in implementing a BTO strategy in a specific setting and from primarily an operations management perspective was investigated. As such, the findings can be complemented by viewing the case study results through the lens of other established general management theories or by replicating the study in different research settings. Originality/value – While past research informs us about how manufacturing firms can successfully achieve mix flexibility or volume flexibility, there are few insights for understanding how volume flexibility and mix flexibility can both be simultaneously achieved within a manufacturing plant and its supply chain. This research fills this gap in the literature and contributes to the development of a theory of BTO strategy implementation, especially in terms of volume flexibility, mix flexibility and their interactions. Keywords Bespoke production, Mass customization, Response flexibility Paper type Research paper

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1. Introduction From consumer electronics (Trommer and Robertson, 1997) to automobile (Howard et al., 2001) to apparel (Aichlmayr, 2001), firms in these and many more industries are striving to adopt what has been dubbed a “build-to-order” (BTO) strategy. A BTO strategy, literally, reflects the idea that value-adding activities – be they distribution, assembly, manufacturing and/or even design – are triggered by customer orders, rather than by forecasts. By performing these value-adding activities to order, a company would accordingly be able to avoid incurring the risks of forecasting uncertain events, which may ultimately lead to excess inventory and poor-service levels (Gunasekaran and Ngai, 2005). A BTO strategy is evidently more attractive when forecasting is difficult and forecast errors are large, i.e. when the market served by a firm is turbulent (Anderson, 2004). Market turbulence, from an operational perspective, leads to volume variability and mix variability in the output of the firm. Whereas, volume variability refers to the gross fluctuation of orders for a certain class of products across time, mix variability refers to the fluctuation of the mix of ordered items across time. A BTO strategy, therefore, requires a supply chain that is both: (1) volume-flexible., i.e. It can rapidly change its gross output while maintaining cost-effectiveness (Sethi and Sethi, 1990; Khouja, 1997; Jack and Raturi, 2002); and (2) mix-flexible., i.e. It can rapidly change the mix of items being delivered to the market while maintaining cost-effectiveness (Berry and Cooper, 1999; Zhang et al., 2003). Mix flexibility, in turn, requires both process flexibility and product flexibility. Process flexibility “refers to the speed at which the company can make decisions, alter schedules or amend existing orders to meet customer needs” (Holweg and Pil, 2001, p. 76). Product flexibility, on the other hand, “relates to how well a company adapts the product to the customer’s specifications and how much it is able to delay or reduce the degree of product tailoring” (Holweg and Pil, 2001, p. 79). But what does it take to implement a BTO strategy? Undoubtedly, insights into the implementation of a BTO strategy can be found in past research on how manufacturing firms can successfully achieve mix flexibility (Ho and Tang, 1998) or volume flexibility (Khouja, 1997; Jack and Raturi, 2002). Yet, understanding how mix flexibility or volume flexibility can be increased may not be sufficient, since the pursuit of a BTO strategy fundamentally forces a firm to strive to simultaneously achieve both volume flexibility and mix flexibility. From both a theoretical and a pragmatic standpoint, it becomes important, therefore, to address the following research questions: RQ1. Are there any interactions between principles and/or practices supporting volume flexibility and those supporting mix flexibility? RQ2. If so, when are these principles and/or practices in trade-off and when do they work synergistically? By seeking answers to these two research questions, we should be able to pinpoint more specifically the extent to which, and the conditions under which, volume flexibility and mix flexibility can be simultaneously pursued within a manufacturing

plant and its supply chain. These answers should, in turn, lead to identification of theoretically and pragmatically relevant insights that ultimately would support the implementation of a BTO strategy, especially in terms of volume flexibility, mix flexibility and their interactions. To provide insights into our research questions, we engaged in an in-depth case study to investigate the factors enabling and hindering the simultaneous pursuit of volume flexibility and mix flexibility within a manufacturing plant and its supply chain. The manufacturing plant under scrutiny manufactures equipment for the Lawn & Garden market. In Section 2, we outline the research methodology applied in this study. Then, in Section 3, we describe the research setting, emphasizing the requirements of the research setting as to the volume variability and the mix variability facing this particular manufacturing plant and its supply chain. In Section 4, we report and discuss speculative insights provided by various informants within the manufacturing plant – speculative insights as to actions and initiatives that may be undertaken as part of implementing a BTO strategy. We propose a synthesis of the findings of the study and sketch future research directions in Section 5. 2. Method From the outset, a qualitative research approach appeared to be most appropriate in order to address the research questions focusing on the implementation of a BTO strategy. Such an approach would unveil not only what interactions might possibly take place between the principles enabling volume flexibility and those supporting mix flexibility but also “how” such interactions might come about (Creswell, 2002). The manufacturing plant selected for this exploratory empirical inquiry offered an ideal research setting, given the corporate decision to pursue a BTO strategy. We timed the collection of interview and archival data to occur after the decision had been made to pursue implementation of a BTO strategy. The decision itself was made after key informants within the manufacturing plant had had sufficient time to review issues and opportunities presented by a BTO strategy. By doing so, we were able to jointly explore with the key informants all the hypothetical options supporting volume flexibility and/or mix flexibility, as well as their interactions, regardless of eventual constraints to the feasibility of each option. In selecting the key informants, we deliberately tried to allow for multiple and divergent perspectives with respect to issues related to implementing a BTO strategy within the manufacturing plant. We identified and targeted the plant manager, the accounting manager, relevant product design engineers, relevant manufacturing process engineers and relevant supply management specialists for interview data collection. Interviews were conducted onsite by two or more members of the research team and were tape-recorded to avoid loss of information or distortion of meaning and to allow for an assessment and verification of content validity after transcription (Rubin and Rubin, 1998; Kvale, 1996). We developed an interview protocol consisting of a set of open-ended questions, exploring what solutions the key informants considered in order to implement a BTO strategy, as well as what obstacles they foresaw in the pursuit of such solutions. We asked, to the extent possible, the same questions of multiple informants in order to increase the reliability of the collected interview data (Jick, 1979). Moreover, we also collected data from a second-fundamental source of information, namely archival documents, internal presentations, internal and official

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reports, etc. We triangulated the interview data from key informants with the data from the secondary information sources, so as to further strengthen the reliability of the collected data (Scandura and Williams, 2000). When data collection had concluded, we performed intra-case analyses adopting the coding techniques recommended by Strauss (1987). We first clustered interview data into large conceptual categories (open coding) and subsequently identified sub-categories (axial coding), according to an indented coding scheme (Rubin and Rubin, 1998). The coding scheme included a description of the current architecture of the product, as well as of the current configuration of the supply chain. An additional part of the codes covered the reasons why the company had chosen to pursue a BTO strategy, the possible options for implementing a BTO strategy, and the obstacles to the implementations of these options, all according to the key informants. 3. The research setting 3.1 Market trends The manufacturing plant selected for the case study, which we henceforth refer to as “LawnWorks,” manufactures lawn tractors, a piece of equipment for the Lawn & Garden Market (see Figure 1 for a schematic drawing of a lawn tractor manufactured by a competitor). The Lawn & Garden Market, particularly in the USA, is a highly-competitive industry characterized by large competitors, relatively mature products, and slowing growth. Up until the 2000-2001 fiscal year, sales of equipment for the Lawn & Garden Market has been growing, on average, 2.7-4.7 percent year-to-year. However, in 2001, a number of events led to a reversal of this trend in sales growth, including the stock-market implosion and economic recession at the beginning of the first quarter, the extreme and unpredictable weather patterns leading to extended drought and flooding conditions during the typical sales season, and the unfortunate terrorist-related event on September 11, 2001. In fact, for 2000-2001, the decline in sales for the entire equipment segment of the Lawn & Garden Market was estimated to be 5.5 percent. 3.2 Seasonality Equipment sales in this market segment have been, and continue to be, structurally and inherently affected by severe seasonality, with consumers typically buying Steering Column

Mower Deck Engaging Lever

Fender Deck

Engine Frame

Figure 1. Example of schematic representation of a lawn tractor

Mower Deck Source: www.Husqvarna.com

equipment “in season” when needed or postponing their purchase decisions until the next season. Approximately, 67 percent of total equipment sales are generally transacted during the spring and early summer months. For equipment sales, manufacturers such as LawnWorks, a 1:7 ratio, on average, is therefore, not atypical when comparing the month with the lowest demand level to that with peak demand level (Figure 2). This ratio may well fluctuate between 1:4 and 1:10, depending on the specific product family considered. For LawnWorks, the seasonality challenge has historically translated into finding ways to increase capacity without incurring fixed costs.

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3.3 Product families and product variants Besides the seasonality challenge, LawnWorks faces additional uncertainty and complexity in serving the Lawn & Garden Market, some of which stems from the heterogeneity of customer needs. An essential distinction needs to be made between heavy-duty and regular equipment. Heavy-duty equipment is more robust and more durable, with greater versatility and operational capabilities. In contrast, regular equipment is neither as robust nor as durable and has less versatility and operational capabilities. While all equipment is designed for a ten-year replacement cycle, heavy-duty equipment is designed to work for more than 1,200 hours, roughly four times more than regular equipment (Figure 3). Therefore, the price would vary accordingly (from approximately $2,000 up to more than 10,000). The ten product families that LawnWorks offe tos on the market span the continuum from heavy-duty equipment to regular domestic versions conceived for the most price sensitive segments of this market. Blanketing the price spectrum with so many product families has been historically a great concern for the marketing function, which has influenced, and continues to influence, new model designs to meet all possible price points observed in the marketplace. For each product family offered by LawnWorks, a set of product models can be selected by the customer. Typically, product variants would differ across a product family in terms of engine power and brand, size, servo mechanisms, tyre size and other

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